Overview

Dataset statistics

Number of variables34
Number of observations133186
Missing cells2227561
Missing cells (%)49.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory124.6 MiB
Average record size in memory980.6 B

Variable types

CAT14
NUM12
UNSUPPORTED8

Warnings

Serial Number has a high cardinality: 73304 distinct values High cardinality
MFR MDL Code has a high cardinality: 16468 distinct values High cardinality
Name has a high cardinality: 76125 distinct values High cardinality
Street 1 has a high cardinality: 69976 distinct values High cardinality
Street2 has a high cardinality: 1617 distinct values High cardinality
City has a high cardinality: 10931 distinct values High cardinality
State has a high cardinality: 58 distinct values High cardinality
ZIP has a high cardinality: 65555 distinct values High cardinality
Country has a high cardinality: 62 distinct values High cardinality
Certification Requested has a high cardinality: 181 distinct values High cardinality
Expiration Date is highly correlated with Last Activity DateHigh correlation
Last Activity Date is highly correlated with Expiration DateHigh correlation
Region is highly correlated with StateHigh correlation
State is highly correlated with Region and 1 other fieldsHigh correlation
Country is highly correlated with StateHigh correlation
Serial Number has 43927 (33.0%) missing values Missing
MFR MDL Code has 43927 (33.0%) missing values Missing
Eng MFR Code has 54315 (40.8%) missing values Missing
Year MFR has 57914 (43.5%) missing values Missing
Type Registrant has 133186 (100.0%) missing values Missing
Street 1 has 27261 (20.5%) missing values Missing
Street2 has 126188 (94.7%) missing values Missing
City has 26966 (20.2%) missing values Missing
State has 27500 (20.6%) missing values Missing
ZIP has 27075 (20.3%) missing values Missing
Region has 26954 (20.2%) missing values Missing
County has 27669 (20.8%) missing values Missing
Country has 26967 (20.2%) missing values Missing
Last Activity Date has 43927 (33.0%) missing values Missing
Cert Issue Date has 51433 (38.6%) missing values Missing
Certification Requested has 55743 (41.9%) missing values Missing
Type Aircraft has 43927 (33.0%) missing values Missing
Type Engine has 43927 (33.0%) missing values Missing
Fractional Ownership has 133165 (> 99.9%) missing values Missing
Airworthiness Date has 62700 (47.1%) missing values Missing
Other Name 1 has 133186 (100.0%) missing values Missing
Other Name 2 has 133186 (100.0%) missing values Missing
Other Name 3 has 133186 (100.0%) missing values Missing
Other Name 4 has 133186 (100.0%) missing values Missing
Other Name 5 has 133186 (100.0%) missing values Missing
Expiration Date has 36507 (27.4%) missing values Missing
Unique ID has 43927 (33.0%) missing values Missing
Kit MFR Code has 129937 (97.6%) missing values Missing
Kit Model has 133186 (100.0%) missing values Missing
Mose S Code Hex has 133186 (100.0%) missing values Missing
Year MFR is highly skewed (γ1 = -30.04201046) Skewed
N-Number has unique values Unique
Type Registrant is an unsupported type, check if it needs cleaning or further analysis Unsupported
Other Name 1 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Other Name 2 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Other Name 3 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Other Name 4 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Other Name 5 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Kit Model is an unsupported type, check if it needs cleaning or further analysis Unsupported
Mose S Code Hex is an unsupported type, check if it needs cleaning or further analysis Unsupported
Eng MFR Code has 2725 (2.0%) zeros Zeros
Type Engine has 3100 (2.3%) zeros Zeros

Reproduction

Analysis started2022-01-09 07:19:48.314420
Analysis finished2022-01-09 07:20:12.236386
Duration23.92 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

N-Number
Categorical

UNIQUE

Distinct133186
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1AJ
 
1
787WA
 
1
591HW
 
1
930MV
 
1
930BZ
 
1
Other values (133181)
133181 
ValueCountFrequency (%) 
1AJ 1< 0.1%
 
787WA1< 0.1%
 
591HW1< 0.1%
 
930MV1< 0.1%
 
930BZ1< 0.1%
 
930RS1< 0.1%
 
930XS1< 0.1%
 
930DX1< 0.1%
 
2841P1< 0.1%
 
28251< 0.1%
 
28331< 0.1%
 
2823P1< 0.1%
 
2749C1< 0.1%
 
1701B1< 0.1%
 
5652L1< 0.1%
 
824B 1< 0.1%
 
906041< 0.1%
 
154AN1< 0.1%
 
79PG 1< 0.1%
 
390RA1< 0.1%
 
622QP1< 0.1%
 
2834M1< 0.1%
 
509PT1< 0.1%
 
330AA1< 0.1%
 
823981< 0.1%
 
Other values (133161)133161> 99.9%
 
2022-01-09T01:20:12.496470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique133186 ?
Unique (%)100.0%
2022-01-09T01:20:12.584938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length4.978413647
Min length1

Overview of Unicode Properties

Unique unicode characters35
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1553678.4%
 
2536528.1%
 
3496837.5%
 
5490517.4%
 
4482097.3%
 
7477607.2%
 
6466527.0%
 
8463457.0%
 
9452046.8%
 
0404156.1%
 
188262.8%
 
A112381.7%
 
C96121.4%
 
S92751.4%
 
B88491.3%
 
M85301.3%
 
D81541.2%
 
T77831.2%
 
P77211.2%
 
R75881.1%
 
H70221.1%
 
J69311.0%
 
W68411.0%
 
E66321.0%
 
G65211.0%
 
Other values (10)491947.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number48233872.7%
 
Uppercase Letter16189124.4%
 
Space Separator188262.8%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
15536711.5%
 
25365211.1%
 
34968310.3%
 
54905110.2%
 
44820910.0%
 
7477609.9%
 
6466529.7%
 
8463459.6%
 
9452049.4%
 
0404158.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A112386.9%
 
C96125.9%
 
S92755.7%
 
B88495.5%
 
M85305.3%
 
D81545.0%
 
T77834.8%
 
P77214.8%
 
R75884.7%
 
H70224.3%
 
J69314.3%
 
W68414.2%
 
E66324.1%
 
G65214.0%
 
F62523.9%
 
K62013.8%
 
L61933.8%
 
V53073.3%
 
N51593.2%
 
X48213.0%
 
U43082.7%
 
Z39582.4%
 
Y35332.2%
 
Q34622.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
18826100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common50116475.6%
 
Latin16189124.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
15536711.0%
 
25365210.7%
 
3496839.9%
 
5490519.8%
 
4482099.6%
 
7477609.5%
 
6466529.3%
 
8463459.2%
 
9452049.0%
 
0404158.1%
 
188263.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A112386.9%
 
C96125.9%
 
S92755.7%
 
B88495.5%
 
M85305.3%
 
D81545.0%
 
T77834.8%
 
P77214.8%
 
R75884.7%
 
H70224.3%
 
J69314.3%
 
W68414.2%
 
E66324.1%
 
G65214.0%
 
F62523.9%
 
K62013.8%
 
L61933.8%
 
V53073.3%
 
N51593.2%
 
X48213.0%
 
U43082.7%
 
Z39582.4%
 
Y35332.2%
 
Q34622.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII663055100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1553678.4%
 
2536528.1%
 
3496837.5%
 
5490517.4%
 
4482097.3%
 
7477607.2%
 
6466527.0%
 
8463457.0%
 
9452046.8%
 
0404156.1%
 
188262.8%
 
A112381.7%
 
C96121.4%
 
S92751.4%
 
B88491.3%
 
M85301.3%
 
D81541.2%
 
T77831.2%
 
P77211.2%
 
R75881.1%
 
H70221.1%
 
J69311.0%
 
W68411.0%
 
E66321.0%
 
G65211.0%
 
Other values (10)491947.4%
 

Serial Number
Categorical

HIGH CARDINALITY
MISSING

Distinct73304
Distinct (%)82.1%
Missing43927
Missing (%)33.0%
Memory size1.0 MiB
1
 
1173
2
 
162
3
 
112
4
 
67
5
 
66
Other values (73299)
87679 
ValueCountFrequency (%) 
111730.9%
 
21620.1%
 
31120.1%
 
4670.1%
 
566< 0.1%
 
762< 0.1%
 
10161< 0.1%
 
100152< 0.1%
 
650< 0.1%
 
1039< 0.1%
 
10038< 0.1%
 
836< 0.1%
 
1132< 0.1%
 
1232< 0.1%
 
3831< 0.1%
 
1829< 0.1%
 
1628< 0.1%
 
928< 0.1%
 
10328< 0.1%
 
1427< 0.1%
 
1927< 0.1%
 
10726< 0.1%
 
10226< 0.1%
 
4226< 0.1%
 
1325< 0.1%
 
Other values (73279)8697665.3%
 
(Missing)4392733.0%
 
2022-01-09T01:20:12.784471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique68111 ?
Unique (%)76.3%
2022-01-09T01:20:12.870014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length5
Mean length12.01735918
Min length1

Overview of Unicode Properties

Unique unicode characters62
Unique unicode categories10 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
90060256.3%
 
n879445.5%
 
1757864.7%
 
2674704.2%
 
0643184.0%
 
7456072.8%
 
5454262.8%
 
a439622.7%
 
8420142.6%
 
3417322.6%
 
4386642.4%
 
6382812.4%
 
-316352.0%
 
9285991.8%
 
A69110.4%
 
C65630.4%
 
D41590.3%
 
R32610.2%
 
B29480.2%
 
S24590.2%
 
T24440.2%
 
E23320.1%
 
P21280.1%
 
H17290.1%
 
F14340.1%
 
Other values (37)121360.8%
 

Most occurring categories

ValueCountFrequency (%) 
Space Separator90060256.3%
 
Decimal Number48789730.5%
 
Lowercase Letter1327418.3%
 
Uppercase Letter468912.9%
 
Dash Punctuation316352.0%
 
Other Punctuation717< 0.1%
 
Math Symbol31< 0.1%
 
Open Punctuation10< 0.1%
 
Close Punctuation10< 0.1%
 
Connector Punctuation10< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
17578615.5%
 
26747013.8%
 
06431813.2%
 
7456079.3%
 
5454269.3%
 
8420148.6%
 
3417328.6%
 
4386647.9%
 
6382817.8%
 
9285995.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A691114.7%
 
C656314.0%
 
D41598.9%
 
R32617.0%
 
B29486.3%
 
S24595.2%
 
T24445.2%
 
E23325.0%
 
P21284.5%
 
H17293.7%
 
F14343.1%
 
M13923.0%
 
L13342.8%
 
G10862.3%
 
W10632.3%
 
J8711.9%
 
V7821.7%
 
U7681.6%
 
N6871.5%
 
K6691.4%
 
X5981.3%
 
I5671.2%
 
O3070.7%
 
Y1760.4%
 
Q1250.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
900602100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-31635100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n8794466.3%
 
a4396233.1%
 
e2770.2%
 
c2720.2%
 
u1420.1%
 
l55< 0.1%
 
y17< 0.1%
 
o16< 0.1%
 
v16< 0.1%
 
r11< 0.1%
 
p9< 0.1%
 
g9< 0.1%
 
b6< 0.1%
 
t5< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.44862.5%
 
/25335.3%
 
:81.1%
 
#71.0%
 
&10.1%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(10100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)10100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+2890.3%
 
=39.7%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_10100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common142091288.8%
 
Latin17963211.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
90060263.4%
 
1757865.3%
 
2674704.7%
 
0643184.5%
 
7456073.2%
 
5454263.2%
 
8420143.0%
 
3417322.9%
 
4386642.7%
 
6382812.7%
 
-316352.2%
 
9285992.0%
 
.448< 0.1%
 
/253< 0.1%
 
+28< 0.1%
 
(10< 0.1%
 
)10< 0.1%
 
_10< 0.1%
 
:8< 0.1%
 
#7< 0.1%
 
=3< 0.1%
 
&1< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n8794449.0%
 
a4396224.5%
 
A69113.8%
 
C65633.7%
 
D41592.3%
 
R32611.8%
 
B29481.6%
 
S24591.4%
 
T24441.4%
 
E23321.3%
 
P21281.2%
 
H17291.0%
 
F14340.8%
 
M13920.8%
 
L13340.7%
 
G10860.6%
 
W10630.6%
 
J8710.5%
 
V7820.4%
 
U7680.4%
 
N6870.4%
 
K6690.4%
 
X5980.3%
 
I5670.3%
 
O3070.2%
 
Other values (15)12340.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1600544100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
90060256.3%
 
n879445.5%
 
1757864.7%
 
2674704.2%
 
0643184.0%
 
7456072.8%
 
5454262.8%
 
a439622.7%
 
8420142.6%
 
3417322.6%
 
4386642.4%
 
6382812.4%
 
-316352.0%
 
9285991.8%
 
A69110.4%
 
C65630.4%
 
D41590.3%
 
R32610.2%
 
B29480.2%
 
S24590.2%
 
T24440.2%
 
E23320.1%
 
P21280.1%
 
H17290.1%
 
F14340.1%
 
Other values (37)121360.8%
 

MFR MDL Code
Categorical

HIGH CARDINALITY
MISSING

Distinct16468
Distinct (%)18.4%
Missing43927
Missing (%)33.0%
Memory size1.0 MiB
7100510
 
1504
7102802
 
1267
2072418
 
1000
7102808
 
958
2110102
 
936
Other values (16463)
83594 
ValueCountFrequency (%) 
710051015041.1%
 
710280212671.0%
 
207241810000.8%
 
71028089580.7%
 
21101029360.7%
 
20724348800.7%
 
21300017840.6%
 
20716027070.5%
 
20724026120.5%
 
81901045830.4%
 
20758165780.4%
 
71018285630.4%
 
71028075570.4%
 
71022105420.4%
 
20718355410.4%
 
71012025240.4%
 
20718264790.4%
 
061008I4660.3%
 
20723064620.3%
 
20724394610.3%
 
88503164550.3%
 
06101C24430.3%
 
20718304380.3%
 
11516044350.3%
 
71028034300.3%
 
Other values (16443)7265454.6%
 
(Missing)4392733.0%
 
2022-01-09T01:20:13.001127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique13255 ?
Unique (%)14.9%
2022-01-09T01:20:13.096429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length7
Mean length5.645758563
Min length3

Overview of Unicode Properties

Unique unicode characters40
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
014735519.6%
 
19624812.8%
 
29369612.5%
 
n8785411.7%
 
7533407.1%
 
a439275.8%
 
5391655.2%
 
6382005.1%
 
3381035.1%
 
8374645.0%
 
4363934.8%
 
9158912.1%
 
C18480.2%
 
N13740.2%
 
I13460.2%
 
A13200.2%
 
B11870.2%
 
Y9380.1%
 
K9160.1%
 
P9080.1%
 
G8970.1%
 
M8880.1%
 
D8780.1%
 
J8720.1%
 
L8540.1%
 
Other values (15)100741.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number59585579.2%
 
Lowercase Letter13178117.5%
 
Uppercase Letter241143.2%
 
Other Punctuation93< 0.1%
 
Math Symbol93< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
014735524.7%
 
19624816.2%
 
29369615.7%
 
7533409.0%
 
5391656.6%
 
6382006.4%
 
3381036.4%
 
8374646.3%
 
4363936.1%
 
9158912.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C18487.7%
 
N13745.7%
 
I13465.6%
 
A13205.5%
 
B11874.9%
 
Y9383.9%
 
K9163.8%
 
P9083.8%
 
G8973.7%
 
M8883.7%
 
D8783.6%
 
J8723.6%
 
L8543.5%
 
E8493.5%
 
R8383.5%
 
F8323.5%
 
X8043.3%
 
H7973.3%
 
U7713.2%
 
V7643.2%
 
Z7503.1%
 
S7293.0%
 
Q7153.0%
 
T6952.9%
 
O6952.9%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n8785466.7%
 
a4392733.3%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.93100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+93100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common59604179.3%
 
Latin15589520.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
014735524.7%
 
19624816.1%
 
29369615.7%
 
7533408.9%
 
5391656.6%
 
6382006.4%
 
3381036.4%
 
8374646.3%
 
4363936.1%
 
9158912.7%
 
.93< 0.1%
 
+93< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n8785456.4%
 
a4392728.2%
 
C18481.2%
 
N13740.9%
 
I13460.9%
 
A13200.8%
 
B11870.8%
 
Y9380.6%
 
K9160.6%
 
P9080.6%
 
G8970.6%
 
M8880.6%
 
D8780.6%
 
J8720.6%
 
L8540.5%
 
E8490.5%
 
R8380.5%
 
F8320.5%
 
X8040.5%
 
H7970.5%
 
U7710.5%
 
V7640.5%
 
Z7500.5%
 
S7290.5%
 
Q7150.5%
 
Other values (3)20391.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII751936100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
014735519.6%
 
19624812.8%
 
29369612.5%
 
n8785411.7%
 
7533407.1%
 
a439275.8%
 
5391655.2%
 
6382005.1%
 
3381035.1%
 
8374645.0%
 
4363934.8%
 
9158912.1%
 
C18480.2%
 
N13740.2%
 
I13460.2%
 
A13200.2%
 
B11870.2%
 
Y9380.1%
 
K9160.1%
 
P9080.1%
 
G8970.1%
 
M8880.1%
 
D8780.1%
 
J8720.1%
 
L8540.1%
 
Other values (15)100741.3%
 

Eng MFR Code
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1481
Distinct (%)1.9%
Missing54315
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean32923.49603
Minimum0
Maximum99999
Zeros2725
Zeros (%)2.0%
Memory size1.0 MiB
2022-01-09T01:20:13.182616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9050
Q117022
median41505
Q341532
95-th percentile59050
Maximum99999
Range99999
Interquartile range (IQR)24510

Descriptive statistics

Standard deviation18955.48391
Coefficient of variation (CV)0.5757433506
Kurtosis1.812383558
Mean32923.49603
Median Absolute Deviation (MAD)14472
Skewness0.9024366545
Sum2596709055
Variance359310370.4
MonotocityNot monotonic
2022-01-09T01:20:13.276821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4150884596.4%
 
1700343723.3%
 
1702636772.8%
 
4151435402.7%
 
1702030172.3%
 
027252.0%
 
1703225571.9%
 
4150524091.8%
 
1700822851.7%
 
1702220711.6%
 
9999919291.4%
 
4151517081.3%
 
4153215881.2%
 
4153313231.0%
 
1702712460.9%
 
4150610290.8%
 
1704010020.8%
 
415309640.7%
 
520089330.7%
 
170098970.7%
 
170058860.7%
 
90507960.6%
 
170126770.5%
 
170426700.5%
 
555726310.5%
 
Other values (1456)2748020.6%
 
(Missing)5431540.8%
 
ValueCountFrequency (%) 
027252.0%
 
4015< 0.1%
 
4023< 0.1%
 
5002< 0.1%
 
5011< 0.1%
 
5112< 0.1%
 
10012< 0.1%
 
100226< 0.1%
 
10071< 0.1%
 
13009< 0.1%
 
ValueCountFrequency (%) 
9999919291.4%
 
833591< 0.1%
 
800005< 0.1%
 
750012< 0.1%
 
720008< 0.1%
 
700042< 0.1%
 
672701< 0.1%
 
672091< 0.1%
 
672052< 0.1%
 
672041< 0.1%
 

Year MFR
Real number (ℝ≥0)

MISSING
SKEWED

Distinct104
Distinct (%)0.1%
Missing57914
Missing (%)43.5%
Infinite0
Infinite (%)0.0%
Mean1973.325513
Minimum0
Maximum2016
Zeros47
Zeros (%)< 0.1%
Memory size1.0 MiB
2022-01-09T01:20:13.371131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1943
Q11958
median1973
Q31993
95-th percentile2013
Maximum2016
Range2016
Interquartile range (IQR)35

Descriptive statistics

Standard deviation54.79692543
Coefficient of variation (CV)0.02776882226
Kurtosis1076.704885
Mean1973.325513
Median Absolute Deviation (MAD)17
Skewness-30.04201046
Sum148536158
Variance3002.703037
MonotocityNot monotonic
2022-01-09T01:20:13.480954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
194651483.9%
 
201521111.6%
 
197820211.5%
 
196620141.5%
 
197619441.5%
 
197719201.4%
 
194718951.4%
 
197917911.3%
 
196817591.3%
 
196717181.3%
 
197516801.3%
 
196515691.2%
 
197415471.2%
 
197315361.2%
 
196914761.1%
 
200713031.0%
 
197212610.9%
 
196412470.9%
 
200611500.9%
 
195911430.9%
 
200511330.9%
 
200810850.8%
 
198110780.8%
 
198010740.8%
 
194110040.8%
 
Other values (79)3366525.3%
 
(Missing)5791443.5%
 
ValueCountFrequency (%) 
047< 0.1%
 
1951< 0.1%
 
1961< 0.1%
 
19101< 0.1%
 
19111< 0.1%
 
19161< 0.1%
 
19172< 0.1%
 
19182< 0.1%
 
19201< 0.1%
 
19221< 0.1%
 
ValueCountFrequency (%) 
20169700.7%
 
201521111.6%
 
20145730.4%
 
20133600.3%
 
20126890.5%
 
20112680.2%
 
20104060.3%
 
20097780.6%
 
200810850.8%
 
200713031.0%
 

Type Registrant
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing133186
Missing (%)100.0%
Memory size1.0 MiB

Name
Categorical

HIGH CARDINALITY

Distinct76125
Distinct (%)57.2%
Missing215
Missing (%)0.2%
Memory size1.0 MiB
CANCELLED/NOT ASSIGNED
26749 
SALE REPORTED
 
884
REGISTRATION PENDING
 
780
WELLS FARGO BANK NORTHWEST NA TRUSTEE
 
765
SOUTHWEST AIRLINES CO
 
533
Other values (76120)
103260 
ValueCountFrequency (%) 
CANCELLED/NOT ASSIGNED 2674920.1%
 
SALE REPORTED 8840.7%
 
REGISTRATION PENDING 7800.6%
 
WELLS FARGO BANK NORTHWEST NA TRUSTEE 7650.6%
 
SOUTHWEST AIRLINES CO 5330.4%
 
TEXTRON AVIATION INC 4490.3%
 
AMERICAN AIRLINES INC 4050.3%
 
GULFSTREAM AEROSPACE CORP 3770.3%
 
SHORT-N-NUMBERS 3090.2%
 
BANK OF UTAH TRUSTEE 3010.2%
 
DELTA AIR LINES INC 2790.2%
 
FEDERAL EXPRESS CORP 2700.2%
 
CESSNA AIRCRAFT COMPANY 2400.2%
 
UNITED AIRLINES INC 2350.2%
 
AIRCRAFT GUARANTY CORP TRUSTEE 2260.2%
 
CIRRUS DESIGN CORP 1950.1%
 
BOEING CO 1760.1%
 
NETJETS SALES INC 1730.1%
 
SOUTHERN AIRCRAFT CONSULTANCY INC TRUSTEE 1520.1%
 
PIPER AIRCRAFT INC 1440.1%
 
AIR TRACTOR INC 1380.1%
 
WILMINGTON TRUST CO TRUSTEE 1260.1%
 
CESSNA AIRCRAFT CO 1230.1%
 
CIVIL AIR PATROL 1190.1%
 
US DOI OFFICE OF AVIATION SERVICES 1070.1%
 
Other values (76100)9871674.1%
 
(Missing)2150.2%
 
2022-01-09T01:20:13.684910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique66732 ?
Unique (%)50.2%
2022-01-09T01:20:13.779112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length50
Median length50
Mean length49.92412866
Min length3

Overview of Unicode Properties

Unique unicode characters52
Unique unicode categories9 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
442279766.5%
 
E2434873.7%
 
N2092873.1%
 
A2081893.1%
 
L1731462.6%
 
I1597682.4%
 
S1497372.3%
 
C1464452.2%
 
R1451232.2%
 
O1285901.9%
 
T1241371.9%
 
D1036961.6%
 
G619540.9%
 
H483650.7%
 
M450300.7%
 
U341960.5%
 
P330370.5%
 
B277220.4%
 
/270280.4%
 
Y264090.4%
 
F246190.4%
 
W238720.4%
 
V235520.4%
 
K218620.3%
 
J199910.3%
 
Other values (27)171560.3%
 

Most occurring categories

ValueCountFrequency (%) 
Space Separator442279766.5%
 
Uppercase Letter218991832.9%
 
Other Punctuation288340.4%
 
Decimal Number54150.1%
 
Dash Punctuation1476< 0.1%
 
Lowercase Letter645< 0.1%
 
Open Punctuation52< 0.1%
 
Close Punctuation52< 0.1%
 
Math Symbol6< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E24348711.1%
 
N2092879.6%
 
A2081899.5%
 
L1731467.9%
 
I1597687.3%
 
S1497376.8%
 
C1464456.7%
 
R1451236.6%
 
O1285905.9%
 
T1241375.7%
 
D1036964.7%
 
G619542.8%
 
H483652.2%
 
M450302.1%
 
U341961.6%
 
P330371.5%
 
B277221.3%
 
Y264091.2%
 
F246191.1%
 
W238721.1%
 
V235521.1%
 
K218621.0%
 
J199910.9%
 
Z33340.2%
 
X33330.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
4422797100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/2702893.7%
 
&14905.2%
 
'1710.6%
 
.1290.4%
 
#11< 0.1%
 
"2< 0.1%
 
?1< 0.1%
 
@1< 0.1%
 
!1< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
181115.0%
 
280214.8%
 
062211.5%
 
356210.4%
 
55279.7%
 
44939.1%
 
74438.2%
 
84067.5%
 
63837.1%
 
93666.8%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1476100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n43066.7%
 
a21533.3%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(52100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)52100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+6100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common445863267.1%
 
Latin219056332.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E24348711.1%
 
N2092879.6%
 
A2081899.5%
 
L1731467.9%
 
I1597687.3%
 
S1497376.8%
 
C1464456.7%
 
R1451236.6%
 
O1285905.9%
 
T1241375.7%
 
D1036964.7%
 
G619542.8%
 
H483652.2%
 
M450302.1%
 
U341961.6%
 
P330371.5%
 
B277221.3%
 
Y264091.2%
 
F246191.1%
 
W238721.1%
 
V235521.1%
 
K218621.0%
 
J199910.9%
 
Z33340.2%
 
X33330.2%
 
Other values (3)16820.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
442279799.2%
 
/270280.6%
 
&1490< 0.1%
 
-1476< 0.1%
 
1811< 0.1%
 
2802< 0.1%
 
0622< 0.1%
 
3562< 0.1%
 
5527< 0.1%
 
4493< 0.1%
 
7443< 0.1%
 
8406< 0.1%
 
6383< 0.1%
 
9366< 0.1%
 
'171< 0.1%
 
.129< 0.1%
 
(52< 0.1%
 
)52< 0.1%
 
#11< 0.1%
 
+6< 0.1%
 
"2< 0.1%
 
?1< 0.1%
 
@1< 0.1%
 
!1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII6649195100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
442279766.5%
 
E2434873.7%
 
N2092873.1%
 
A2081893.1%
 
L1731462.6%
 
I1597682.4%
 
S1497372.3%
 
C1464452.2%
 
R1451232.2%
 
O1285901.9%
 
T1241371.9%
 
D1036961.6%
 
G619540.9%
 
H483650.7%
 
M450300.7%
 
U341960.5%
 
P330370.5%
 
B277220.4%
 
/270280.4%
 
Y264090.4%
 
F246190.4%
 
W238720.4%
 
V235520.4%
 
K218620.3%
 
J199910.3%
 
Other values (27)171560.3%
 

Street 1
Categorical

HIGH CARDINALITY
MISSING

Distinct69976
Distinct (%)66.1%
Missing27261
Missing (%)20.5%
Memory size1.0 MiB
3511 SILVERSIDE RD STE 105
 
965
1 CESSNA BLVD
 
483
PO BOX 368
 
323
DAL2MX CERTIFICATE COMPLIANCE
 
308
2711 CENTERVILLE RD STE 400
 
296
Other values (69971)
103550 
ValueCountFrequency (%) 
3511 SILVERSIDE RD STE 105 9650.7%
 
1 CESSNA BLVD 4830.4%
 
PO BOX 368 3230.2%
 
DAL2MX CERTIFICATE COMPLIANCE 3080.2%
 
2711 CENTERVILLE RD STE 400 2960.2%
 
200 E SOUTH TEMPLE STE 210 2800.2%
 
C/O DEBEE GILCHRIST PC 2600.2%
 
MAC U1228-51 2440.2%
 
PO BOX 7704 2180.2%
 
PO BOX 2549 1980.1%
 
1775 M H JACKSON SERVICE RD 1850.1%
 
VP SUPPLEMENTAL AIR OPERATIONS 1820.1%
 
4515 TAYLOR CIR 1760.1%
 
1901 OAKESDALE AVE SW 1750.1%
 
16192 COASTAL HWY 1590.1%
 
C/O BUSINESS AIRCRAFT TITLE INTL 1550.1%
 
PO BOX 485 1450.1%
 
2702 LOVE FIELD DR # HDQ-4GC 1430.1%
 
MAC U1228-051 1350.1%
 
2926 PIPER DR 1340.1%
 
C/O IATS 1300.1%
 
444 S RIVER RD 1090.1%
 
C/O DFPH&J 1090.1%
 
3255 BELL HELICOPTER BLVD 1000.1%
 
105 S HANSELL ST 980.1%
 
Other values (69951)10021575.2%
 
(Missing)2726120.5%
 
2022-01-09T01:20:13.951874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique59520 ?
Unique (%)56.2%
2022-01-09T01:20:14.039544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length33
Median length33
Mean length26.85860376
Min length2

Overview of Unicode Properties

Unique unicode characters53
Unique unicode categories9 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
199989655.9%
 
E1040852.9%
 
R1034722.9%
 
A846742.4%
 
O818882.3%
 
1807392.3%
 
T769082.1%
 
S678521.9%
 
D667991.9%
 
N657041.8%
 
0630051.8%
 
L587161.6%
 
n545221.5%
 
2537061.5%
 
I521351.5%
 
5426881.2%
 
3416901.2%
 
C369131.0%
 
4363801.0%
 
P326610.9%
 
B306540.9%
 
H302740.8%
 
6295380.8%
 
7283480.8%
 
a272610.8%
 
Other values (28)2266826.3%
 

Most occurring categories

ValueCountFrequency (%) 
Space Separator199989655.9%
 
Uppercase Letter106108529.7%
 
Decimal Number42685211.9%
 
Lowercase Letter817832.3%
 
Other Punctuation57800.2%
 
Dash Punctuation1779< 0.1%
 
Open Punctuation7< 0.1%
 
Close Punctuation7< 0.1%
 
Math Symbol1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E1040859.8%
 
R1034729.8%
 
A846748.0%
 
O818887.7%
 
T769087.2%
 
S678526.4%
 
D667996.3%
 
N657046.2%
 
L587165.5%
 
I521354.9%
 
C369133.5%
 
P326613.1%
 
B306542.9%
 
H302742.9%
 
W267832.5%
 
V238292.2%
 
M205031.9%
 
U188991.8%
 
Y181291.7%
 
G170221.6%
 
X150901.4%
 
K134911.3%
 
F100991.0%
 
J24050.2%
 
Q10610.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1999896100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
18073918.9%
 
06300514.8%
 
25370612.6%
 
54268810.0%
 
3416909.8%
 
4363808.5%
 
6295386.9%
 
7283486.6%
 
8258376.1%
 
9249215.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n5452266.7%
 
a2726133.3%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/231440.0%
 
#164328.4%
 
:104818.1%
 
&5239.0%
 
.2283.9%
 
%130.2%
 
'70.1%
 
"2< 0.1%
 
@1< 0.1%
 
\1< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1779100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(7100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)7100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common243432268.1%
 
Latin114286831.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E1040859.1%
 
R1034729.1%
 
A846747.4%
 
O818887.2%
 
T769086.7%
 
S678525.9%
 
D667995.8%
 
N657045.7%
 
L587165.1%
 
n545224.8%
 
I521354.6%
 
C369133.2%
 
P326612.9%
 
B306542.7%
 
H302742.6%
 
a272612.4%
 
W267832.3%
 
V238292.1%
 
M205031.8%
 
U188991.7%
 
Y181291.6%
 
G170221.5%
 
X150901.3%
 
K134911.2%
 
F100990.9%
 
Other values (3)45050.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
199989682.2%
 
1807393.3%
 
0630052.6%
 
2537062.2%
 
5426881.8%
 
3416901.7%
 
4363801.5%
 
6295381.2%
 
7283481.2%
 
8258371.1%
 
9249211.0%
 
/23140.1%
 
-17790.1%
 
#16430.1%
 
:1048< 0.1%
 
&523< 0.1%
 
.228< 0.1%
 
%13< 0.1%
 
'7< 0.1%
 
(7< 0.1%
 
)7< 0.1%
 
"2< 0.1%
 
@1< 0.1%
 
\1< 0.1%
 
+1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3577190100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
199989655.9%
 
E1040852.9%
 
R1034722.9%
 
A846742.4%
 
O818882.3%
 
1807392.3%
 
T769082.1%
 
S678521.9%
 
D667991.9%
 
N657041.8%
 
0630051.8%
 
L587161.6%
 
n545221.5%
 
2537061.5%
 
I521351.5%
 
5426881.2%
 
3416901.2%
 
C369131.0%
 
4363801.0%
 
P326610.9%
 
B306540.9%
 
H302740.8%
 
6295380.8%
 
7283480.8%
 
a272610.8%
 
Other values (28)2266826.3%
 

Street2
Categorical

HIGH CARDINALITY
MISSING

Distinct1617
Distinct (%)23.1%
Missing126188
Missing (%)94.7%
Memory size1.0 MiB
1200 NW 63RD ST STE 5000
 
495
299 S MAIN ST FL 5
 
466
2832 SHORECREST DR
 
318
2955 REPUBLICAN DR
 
182
DEPT 095
 
166
Other values (1612)
5371 
ValueCountFrequency (%) 
1200 NW 63RD ST STE 5000 4950.4%
 
299 S MAIN ST FL 5 4660.3%
 
2832 SHORECREST DR 3180.2%
 
2955 REPUBLICAN DR 1820.1%
 
DEPT 095 1660.1%
 
324 N ROBINSON AVE STE 100 1370.1%
 
100 N BROADWAY STE 2000 1320.1%
 
DEPT 595 AIRCRAFT REGISTRATIONS 1220.1%
 
3131 DEMOCRAT RD 1070.1%
 
PO BOX 19527 920.1%
 
PO BOX 66142 850.1%
 
1400 N HURSTBOURNE PKWY 850.1%
 
1100 N MARKET ST 850.1%
 
MAC U1228-051 820.1%
 
233 S WACKER DR 800.1%
 
100 N BROADWAY AVE STE 2000 800.1%
 
300 E MALLARD DR STE 200 760.1%
 
PO BOX 2216 750.1%
 
5501 JOSH BIRMINGHAM PKWY 750.1%
 
DEPT 595 750.1%
 
PO BOX 10212 750.1%
 
PO BOX 81009 750.1%
 
2207 CONCORD PIKE 700.1%
 
MAC U1228-51 54< 0.1%
 
11495 NAVAID RD 54< 0.1%
 
Other values (1592)36552.7%
 
(Missing)12618894.7%
 
2022-01-09T01:20:14.140819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1191 ?
Unique (%)17.0%
2022-01-09T01:20:14.229567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length33
Median length3
Mean length4.574279579
Min length2

Overview of Unicode Properties

Unique unicode characters47
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n25237641.4%
 
a12618820.7%
 
12537020.6%
 
081871.3%
 
R71231.2%
 
E69961.1%
 
S66791.1%
 
T65841.1%
 
A56160.9%
 
152350.9%
 
N51430.8%
 
O50430.8%
 
249810.8%
 
D41890.7%
 
I36630.6%
 
534620.6%
 
325600.4%
 
L25450.4%
 
925110.4%
 
B23460.4%
 
C23420.4%
 
P21680.4%
 
M20390.3%
 
W16260.3%
 
615970.3%
 
Other values (22)126612.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter37856462.1%
 
Space Separator12537020.6%
 
Uppercase Letter7286912.0%
 
Decimal Number319285.2%
 
Dash Punctuation3560.1%
 
Other Punctuation137< 0.1%
 
Open Punctuation3< 0.1%
 
Close Punctuation3< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n25237666.7%
 
a12618833.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0818725.6%
 
1523516.4%
 
2498115.6%
 
5346210.8%
 
325608.0%
 
925117.9%
 
615975.0%
 
412473.9%
 
811903.7%
 
79583.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
125370100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R71239.8%
 
E69969.6%
 
S66799.2%
 
T65849.0%
 
A56167.7%
 
N51437.1%
 
O50436.9%
 
D41895.7%
 
I36635.0%
 
L25453.5%
 
B23463.2%
 
C23423.2%
 
P21683.0%
 
M20392.8%
 
W16262.2%
 
H15912.2%
 
U12451.7%
 
V12321.7%
 
F10741.5%
 
G9431.3%
 
K8261.1%
 
Y8171.1%
 
X7461.0%
 
J1730.2%
 
Z740.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-356100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
:6648.2%
 
#5137.2%
 
/1510.9%
 
'32.2%
 
.21.5%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(3100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)3100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin45143374.1%
 
Common15779725.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n25237655.9%
 
a12618828.0%
 
R71231.6%
 
E69961.5%
 
S66791.5%
 
T65841.5%
 
A56161.2%
 
N51431.1%
 
O50431.1%
 
D41890.9%
 
I36630.8%
 
L25450.6%
 
B23460.5%
 
C23420.5%
 
P21680.5%
 
M20390.5%
 
W16260.4%
 
H15910.4%
 
U12450.3%
 
V12320.3%
 
F10740.2%
 
G9430.2%
 
K8260.2%
 
Y8170.2%
 
X7460.2%
 
Other values (3)2930.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
12537079.5%
 
081875.2%
 
152353.3%
 
249813.2%
 
534622.2%
 
325601.6%
 
925111.6%
 
615971.0%
 
412470.8%
 
811900.8%
 
79580.6%
 
-3560.2%
 
:66< 0.1%
 
#51< 0.1%
 
/15< 0.1%
 
(3< 0.1%
 
)3< 0.1%
 
'3< 0.1%
 
.2< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII609230100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n25237641.4%
 
a12618820.7%
 
12537020.6%
 
081871.3%
 
R71231.2%
 
E69961.1%
 
S66791.1%
 
T65841.1%
 
A56160.9%
 
152350.9%
 
N51430.8%
 
O50430.8%
 
249810.8%
 
D41890.7%
 
I36630.6%
 
534620.6%
 
325600.4%
 
L25450.4%
 
925110.4%
 
B23460.4%
 
C23420.4%
 
P21680.4%
 
M20390.3%
 
W16260.3%
 
615970.3%
 
Other values (22)126612.1%
 

City
Categorical

HIGH CARDINALITY
MISSING

Distinct10931
Distinct (%)10.3%
Missing26966
Missing (%)20.2%
Memory size1.0 MiB
WILMINGTON
 
2480
OKLAHOMA CITY
 
1831
SALT LAKE CITY
 
1202
WICHITA
 
1138
DALLAS
 
967
Other values (10926)
98602 
ValueCountFrequency (%) 
WILMINGTON 24801.9%
 
OKLAHOMA CITY 18311.4%
 
SALT LAKE CITY 12020.9%
 
WICHITA 11380.9%
 
DALLAS 9670.7%
 
ANCHORAGE 7720.6%
 
HOUSTON 6030.5%
 
FORT WORTH 5980.4%
 
ATLANTA 5350.4%
 
MIAMI 4790.4%
 
MEMPHIS 4720.4%
 
CHICAGO 4600.3%
 
PHOENIX 4330.3%
 
ALBUQUERQUE 4050.3%
 
SEATTLE 3740.3%
 
LAS VEGAS 3650.3%
 
SAN ANTONIO 3570.3%
 
LOUISVILLE 3450.3%
 
GROVELAND 3350.3%
 
BOISE 3070.2%
 
LOS ANGELES 3050.2%
 
TUCSON 3030.2%
 
PORTLAND 3010.2%
 
TULSA 2830.2%
 
FAIRBANKS 2810.2%
 
Other values (10906)9028967.8%
 
(Missing)2696620.2%
 
2022-01-09T01:20:14.331711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3763 ?
Unique (%)3.5%
2022-01-09T01:20:14.418125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length18
Mean length14.96287898
Min length3

Overview of Unicode Properties

Unique unicode characters46
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
100665850.5%
 
A944644.7%
 
E851094.3%
 
L750743.8%
 
O745673.7%
 
N727023.6%
 
I611833.1%
 
R590803.0%
 
T551982.8%
 
n539322.7%
 
S511252.6%
 
C321591.6%
 
H284871.4%
 
D275821.4%
 
a269661.4%
 
M263251.3%
 
G229061.1%
 
U209951.1%
 
B187740.9%
 
W186880.9%
 
K165840.8%
 
P164950.8%
 
Y155610.8%
 
V145370.7%
 
F112260.6%
 
Other values (21)64690.3%
 

Most occurring categories

ValueCountFrequency (%) 
Space Separator100665850.5%
 
Uppercase Letter90517145.4%
 
Lowercase Letter808984.1%
 
Decimal Number93< 0.1%
 
Dash Punctuation15< 0.1%
 
Other Punctuation9< 0.1%
 
Open Punctuation1< 0.1%
 
Close Punctuation1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A9446410.4%
 
E851099.4%
 
L750748.3%
 
O745678.2%
 
N727028.0%
 
I611836.8%
 
R590806.5%
 
T551986.1%
 
S511255.6%
 
C321593.6%
 
H284873.1%
 
D275823.0%
 
M263252.9%
 
G229062.5%
 
U209952.3%
 
B187742.1%
 
W186882.1%
 
K165841.8%
 
P164951.8%
 
Y155611.7%
 
V145371.6%
 
F112261.2%
 
J20930.2%
 
X20560.2%
 
Q12900.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1006658100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n5393266.7%
 
a2696633.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-15100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
'444.4%
 
/333.3%
 
&111.1%
 
.111.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11920.4%
 
21415.1%
 
01415.1%
 
51314.0%
 
31010.8%
 
988.6%
 
666.5%
 
444.3%
 
733.2%
 
822.2%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(1100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common100677750.5%
 
Latin98606949.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A944649.6%
 
E851098.6%
 
L750747.6%
 
O745677.6%
 
N727027.4%
 
I611836.2%
 
R590806.0%
 
T551985.6%
 
n539325.5%
 
S511255.2%
 
C321593.3%
 
H284872.9%
 
D275822.8%
 
a269662.7%
 
M263252.7%
 
G229062.3%
 
U209952.1%
 
B187741.9%
 
W186881.9%
 
K165841.7%
 
P164951.7%
 
Y155611.6%
 
V145371.5%
 
F112261.1%
 
J20930.2%
 
Other values (3)42570.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
1006658> 99.9%
 
119< 0.1%
 
-15< 0.1%
 
214< 0.1%
 
014< 0.1%
 
513< 0.1%
 
310< 0.1%
 
98< 0.1%
 
66< 0.1%
 
'4< 0.1%
 
44< 0.1%
 
73< 0.1%
 
/3< 0.1%
 
82< 0.1%
 
&1< 0.1%
 
(1< 0.1%
 
)1< 0.1%
 
.1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1992846100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
100665850.5%
 
A944644.7%
 
E851094.3%
 
L750743.8%
 
O745673.7%
 
N727023.6%
 
I611833.1%
 
R590803.0%
 
T551982.8%
 
n539322.7%
 
S511252.6%
 
C321591.6%
 
H284871.4%
 
D275821.4%
 
a269661.4%
 
M263251.3%
 
G229061.1%
 
U209951.1%
 
B187740.9%
 
W186880.9%
 
K165840.8%
 
P164950.8%
 
Y155610.8%
 
V145370.7%
 
F112260.6%
 
Other values (21)64690.3%
 

State
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct58
Distinct (%)0.1%
Missing27500
Missing (%)20.6%
Memory size1.0 MiB
TX
10003 
CA
9529 
FL
7912 
WA
 
3769
OK
 
3361
Other values (53)
71112 
ValueCountFrequency (%) 
TX100037.5%
 
CA95297.2%
 
FL79125.9%
 
WA37692.8%
 
OK33612.5%
 
DE32092.4%
 
OH30752.3%
 
IL30182.3%
 
MI29252.2%
 
GA28682.2%
 
NY27942.1%
 
AK25811.9%
 
KS25201.9%
 
AZ24851.9%
 
MN24361.8%
 
PA23971.8%
 
NC23811.8%
 
CO23271.7%
 
OR22151.7%
 
UT20971.6%
 
WI20661.6%
 
TN20331.5%
 
VA20181.5%
 
IN17991.4%
 
MO17941.3%
 
Other values (33)2207416.6%
 
(Missing)2750020.6%
 
2022-01-09T01:20:14.511879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2022-01-09T01:20:14.589987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.206478158
Min length2

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n5500018.7%
 
A3199210.9%
 
a275009.4%
 
N171495.8%
 
T164735.6%
 
C161045.5%
 
L138634.7%
 
M130784.5%
 
O127724.3%
 
I126814.3%
 
X100033.4%
 
K94073.2%
 
F79122.7%
 
D68772.3%
 
W67342.3%
 
S47821.6%
 
E45561.6%
 
Y42531.4%
 
V40011.4%
 
H38701.3%
 
R35161.2%
 
G28991.0%
 
P25800.9%
 
Z24850.8%
 
U21280.7%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter21137271.9%
 
Lowercase Letter8250028.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A3199215.1%
 
N171498.1%
 
T164737.8%
 
C161047.6%
 
L138636.6%
 
M130786.2%
 
O127726.0%
 
I126816.0%
 
X100034.7%
 
K94074.5%
 
F79123.7%
 
D68773.3%
 
W67343.2%
 
S47822.3%
 
E45562.2%
 
Y42532.0%
 
V40011.9%
 
H38701.8%
 
R35161.7%
 
G28991.4%
 
P25801.2%
 
Z24851.2%
 
U21281.0%
 
J12570.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n5500066.7%
 
a2750033.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin293872100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n5500018.7%
 
A3199210.9%
 
a275009.4%
 
N171495.8%
 
T164735.6%
 
C161045.5%
 
L138634.7%
 
M130784.5%
 
O127724.3%
 
I126814.3%
 
X100033.4%
 
K94073.2%
 
F79122.7%
 
D68772.3%
 
W67342.3%
 
S47821.6%
 
E45561.6%
 
Y42531.4%
 
V40011.4%
 
H38701.3%
 
R35161.2%
 
G28991.0%
 
P25800.9%
 
Z24850.8%
 
U21280.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII293872100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n5500018.7%
 
A3199210.9%
 
a275009.4%
 
N171495.8%
 
T164735.6%
 
C161045.5%
 
L138634.7%
 
M130784.5%
 
O127724.3%
 
I126814.3%
 
X100033.4%
 
K94073.2%
 
F79122.7%
 
D68772.3%
 
W67342.3%
 
S47821.6%
 
E45561.6%
 
Y42531.4%
 
V40011.4%
 
H38701.3%
 
R35161.2%
 
G28991.0%
 
P25800.9%
 
Z24850.8%
 
U21280.7%
 

ZIP
Categorical

HIGH CARDINALITY
MISSING

Distinct65555
Distinct (%)61.8%
Missing27075
Missing (%)20.3%
Memory size1.0 MiB
198104902
 
1038
0
 
494
672151400
 
492
841112689
 
484
731165706
 
358
Other values (65550)
103245 
ValueCountFrequency (%) 
19810490210380.8%
 
04940.4%
 
6721514004920.4%
 
8411126894840.4%
 
7311657063580.3%
 
3473603683090.2%
 
1980816453010.2%
 
7523519172830.2%
 
8411113462730.2%
 
841112360.2%
 
672772150.2%
 
7736025492090.2%
 
3811815471880.1%
 
3035437431800.1%
 
9805726231790.1%
 
7523519081670.1%
 
4022340151660.1%
 
731251650.1%
 
1995836081620.1%
 
7615526051570.1%
 
3811815161430.1%
 
3296019551430.1%
 
7310264171400.1%
 
731021390.1%
 
761551360.1%
 
Other values (65530)9935474.6%
 
(Missing)2707520.3%
 
2022-01-09T01:20:14.764377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique53106 ?
Unique (%)50.0%
2022-01-09T01:20:14.864323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length9
Mean length7.124847957
Min length1

Overview of Unicode Properties

Unique unicode characters40
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
010991711.6%
 
110475111.0%
 
3929429.8%
 
2907259.6%
 
4819448.6%
 
5819338.6%
 
7794408.4%
 
9776178.2%
 
6739587.8%
 
8726517.7%
 
n541505.7%
 
a270752.9%
 
8860.1%
 
N321< 0.1%
 
R169< 0.1%
 
D156< 0.1%
 
H34< 0.1%
 
A33< 0.1%
 
-24< 0.1%
 
C22< 0.1%
 
L22< 0.1%
 
V19< 0.1%
 
E19< 0.1%
 
G16< 0.1%
 
S12< 0.1%
 
Other values (15)94< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number86587891.2%
 
Lowercase Letter812258.6%
 
Uppercase Letter9170.1%
 
Space Separator8860.1%
 
Dash Punctuation24< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
010991712.7%
 
110475112.1%
 
39294210.7%
 
29072510.5%
 
4819449.5%
 
5819339.5%
 
7794409.2%
 
9776179.0%
 
6739588.5%
 
8726518.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n5415066.7%
 
a2707533.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N32135.0%
 
R16918.4%
 
D15617.0%
 
H343.7%
 
A333.6%
 
C222.4%
 
L222.4%
 
V192.1%
 
E192.1%
 
G161.7%
 
S121.3%
 
J121.3%
 
W101.1%
 
P101.1%
 
K80.9%
 
Y80.9%
 
T70.8%
 
M70.8%
 
O70.8%
 
B60.7%
 
Z50.5%
 
I40.4%
 
U40.4%
 
Q30.3%
 
X20.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
886100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-24100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common86678891.3%
 
Latin821428.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
010991712.7%
 
110475112.1%
 
39294210.7%
 
29072510.5%
 
4819449.5%
 
5819339.5%
 
7794409.2%
 
9776179.0%
 
6739588.5%
 
8726518.4%
 
8860.1%
 
-24< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n5415065.9%
 
a2707533.0%
 
N3210.4%
 
R1690.2%
 
D1560.2%
 
H34< 0.1%
 
A33< 0.1%
 
C22< 0.1%
 
L22< 0.1%
 
V19< 0.1%
 
E19< 0.1%
 
G16< 0.1%
 
S12< 0.1%
 
J12< 0.1%
 
W10< 0.1%
 
P10< 0.1%
 
K8< 0.1%
 
Y8< 0.1%
 
T7< 0.1%
 
M7< 0.1%
 
O7< 0.1%
 
B6< 0.1%
 
Z5< 0.1%
 
I4< 0.1%
 
U4< 0.1%
 
Other values (3)6< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII948930100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
010991711.6%
 
110475111.0%
 
3929429.8%
 
2907259.6%
 
4819448.6%
 
5819338.6%
 
7794408.4%
 
9776178.2%
 
6739587.8%
 
8726517.7%
 
n541505.7%
 
a270752.9%
 
8860.1%
 
N321< 0.1%
 
R169< 0.1%
 
D156< 0.1%
 
H34< 0.1%
 
A33< 0.1%
 
-24< 0.1%
 
C22< 0.1%
 
L22< 0.1%
 
V19< 0.1%
 
E19< 0.1%
 
G16< 0.1%
 
S12< 0.1%
 
Other values (15)94< 0.1%
 

Region
Categorical

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)< 0.1%
Missing26954
Missing (%)20.2%
Memory size1.0 MiB
2
17559 
C
16625 
1
15644 
4
13787 
7
13480 
Other values (5)
29137 
ValueCountFrequency (%) 
21755913.2%
 
C1662512.5%
 
11564411.7%
 
41378710.4%
 
71348010.1%
 
S1345610.1%
 
393357.0%
 
E34062.6%
 
525791.9%
 
83610.3%
 
(Missing)2695420.2%
 
2022-01-09T01:20:14.933388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-01-09T01:20:14.995873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:15.079750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.404757257
Min length1

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n5390828.8%
 
a2695414.4%
 
2175599.4%
 
C166258.9%
 
1156448.4%
 
4137877.4%
 
7134807.2%
 
S134567.2%
 
393355.0%
 
E34061.8%
 
525791.4%
 
83610.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter8086243.2%
 
Decimal Number7274538.9%
 
Uppercase Letter3348717.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C1662549.6%
 
S1345640.2%
 
E340610.2%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
21755924.1%
 
11564421.5%
 
41378719.0%
 
71348018.5%
 
3933512.8%
 
525793.5%
 
83610.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n5390866.7%
 
a2695433.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin11434961.1%
 
Common7274538.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n5390847.1%
 
a2695423.6%
 
C1662514.5%
 
S1345611.8%
 
E34063.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
21755924.1%
 
11564421.5%
 
41378719.0%
 
71348018.5%
 
3933512.8%
 
525793.5%
 
83610.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII187094100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n5390828.8%
 
a2695414.4%
 
2175599.4%
 
C166258.9%
 
1156448.4%
 
4137877.4%
 
7134807.2%
 
S134567.2%
 
393355.0%
 
E34061.8%
 
525791.4%
 
83610.2%
 

County
Real number (ℝ≥0)

MISSING

Distinct324
Distinct (%)0.3%
Missing27669
Missing (%)20.8%
Infinite0
Infinite (%)0.0%
Mean90.54160941
Minimum0
Maximum999
Zeros119
Zeros (%)0.1%
Memory size1.0 MiB
2022-01-09T01:20:15.173480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q127
median63
Q3113
95-th percentile291
Maximum999
Range999
Interquartile range (IQR)86

Descriptive statistics

Standard deviation105.2836205
Coefficient of variation (CV)1.16282029
Kurtosis11.88840334
Mean90.54160941
Median Absolute Deviation (MAD)44
Skewness2.99174998
Sum9553679
Variance11084.64074
MonotocityNot monotonic
2022-01-09T01:20:15.267206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
350183.8%
 
1328352.1%
 
128082.1%
 
3727552.1%
 
10925971.9%
 
3525541.9%
 
3124981.9%
 
11320981.6%
 
519751.5%
 
2519541.5%
 
3319411.5%
 
2918751.4%
 
1118651.4%
 
1916651.3%
 
6116171.2%
 
7315721.2%
 
1715411.2%
 
915221.1%
 
5914381.1%
 
5314341.1%
 
17313251.0%
 
11113091.0%
 
6712941.0%
 
2712871.0%
 
8512711.0%
 
Other values (299)5546941.6%
 
(Missing)2766920.8%
 
ValueCountFrequency (%) 
01190.1%
 
128082.1%
 
21< 0.1%
 
350183.8%
 
44< 0.1%
 
519751.5%
 
69< 0.1%
 
79340.7%
 
915221.1%
 
107< 0.1%
 
ValueCountFrequency (%) 
9992< 0.1%
 
84024< 0.1%
 
83036< 0.1%
 
8205< 0.1%
 
8101080.1%
 
80012< 0.1%
 
7906< 0.1%
 
77511< 0.1%
 
77030< 0.1%
 
760900.1%
 

Country
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct62
Distinct (%)0.1%
Missing26967
Missing (%)20.2%
Memory size1.0 MiB
US
105434 
GB
 
184
RQ
 
173
DE
 
50
VI
 
47
Other values (57)
 
331
ValueCountFrequency (%) 
US10543479.2%
 
GB1840.1%
 
RQ1730.1%
 
DE50< 0.1%
 
VI47< 0.1%
 
CA40< 0.1%
 
GU31< 0.1%
 
CH27< 0.1%
 
FR21< 0.1%
 
AU18< 0.1%
 
VU16< 0.1%
 
AT13< 0.1%
 
PH12< 0.1%
 
SA11< 0.1%
 
NL10< 0.1%
 
MX10< 0.1%
 
NZ9< 0.1%
 
HK8< 0.1%
 
SG7< 0.1%
 
BS7< 0.1%
 
CO6< 0.1%
 
IE6< 0.1%
 
BE6< 0.1%
 
MP5< 0.1%
 
JP4< 0.1%
 
Other values (37)64< 0.1%
 
(Missing)2696720.2%
 
2022-01-09T01:20:15.382344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique20 ?
Unique (%)< 0.1%
2022-01-09T01:20:15.469154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.202476236
Min length2

Overview of Unicode Properties

Unique unicode characters27
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
U10550236.0%
 
S10546236.0%
 
n5393418.4%
 
a269679.2%
 
G2260.1%
 
R2050.1%
 
B2010.1%
 
Q1750.1%
 
A94< 0.1%
 
C78< 0.1%
 
E69< 0.1%
 
V67< 0.1%
 
H59< 0.1%
 
I54< 0.1%
 
D53< 0.1%
 
N29< 0.1%
 
P29< 0.1%
 
T23< 0.1%
 
F22< 0.1%
 
M20< 0.1%
 
K15< 0.1%
 
L14< 0.1%
 
Z12< 0.1%
 
O11< 0.1%
 
X10< 0.1%
 
Other values (2)8< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter21243872.4%
 
Lowercase Letter8090127.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
U10550249.7%
 
S10546249.6%
 
G2260.1%
 
R2050.1%
 
B2010.1%
 
Q1750.1%
 
A94< 0.1%
 
C78< 0.1%
 
E69< 0.1%
 
V67< 0.1%
 
H59< 0.1%
 
I54< 0.1%
 
D53< 0.1%
 
N29< 0.1%
 
P29< 0.1%
 
T23< 0.1%
 
F22< 0.1%
 
M20< 0.1%
 
K15< 0.1%
 
L14< 0.1%
 
Z12< 0.1%
 
O11< 0.1%
 
X10< 0.1%
 
J5< 0.1%
 
Y3< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n5393466.7%
 
a2696733.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin293339100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
U10550236.0%
 
S10546236.0%
 
n5393418.4%
 
a269679.2%
 
G2260.1%
 
R2050.1%
 
B2010.1%
 
Q1750.1%
 
A94< 0.1%
 
C78< 0.1%
 
E69< 0.1%
 
V67< 0.1%
 
H59< 0.1%
 
I54< 0.1%
 
D53< 0.1%
 
N29< 0.1%
 
P29< 0.1%
 
T23< 0.1%
 
F22< 0.1%
 
M20< 0.1%
 
K15< 0.1%
 
L14< 0.1%
 
Z12< 0.1%
 
O11< 0.1%
 
X10< 0.1%
 
Other values (2)8< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII293339100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
U10550236.0%
 
S10546236.0%
 
n5393418.4%
 
a269679.2%
 
G2260.1%
 
R2050.1%
 
B2010.1%
 
Q1750.1%
 
A94< 0.1%
 
C78< 0.1%
 
E69< 0.1%
 
V67< 0.1%
 
H59< 0.1%
 
I54< 0.1%
 
D53< 0.1%
 
N29< 0.1%
 
P29< 0.1%
 
T23< 0.1%
 
F22< 0.1%
 
M20< 0.1%
 
K15< 0.1%
 
L14< 0.1%
 
Z12< 0.1%
 
O11< 0.1%
 
X10< 0.1%
 
Other values (2)8< 0.1%
 

Last Activity Date
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct2620
Distinct (%)2.9%
Missing43927
Missing (%)33.0%
Infinite0
Infinite (%)0.0%
Mean20119973.32
Minimum19720113
Maximum20161229
Zeros0
Zeros (%)0.0%
Memory size1.0 MiB
2022-01-09T01:20:15.547256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19720113
5-th percentile19770114
Q120140807
median20150624
Q320160412
95-th percentile20161118
Maximum20161229
Range441116
Interquartile range (IQR)19605

Descriptive statistics

Standard deviation102396.0033
Coefficient of variation (CV)0.005089271324
Kurtosis7.540917239
Mean20119973.32
Median Absolute Deviation (MAD)9809
Skewness-3.060234901
Sum1.795888699e+12
Variance1.048494148e+10
MonotocityNot monotonic
2022-01-09T01:20:15.651747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1977011460604.6%
 
201612123110.2%
 
201505112870.2%
 
201508112860.2%
 
201505122800.2%
 
201408112670.2%
 
201408122620.2%
 
201612132600.2%
 
201611082590.2%
 
201611152540.2%
 
201611142530.2%
 
201611162450.2%
 
201506082400.2%
 
201405122360.2%
 
201511102320.2%
 
201605102290.2%
 
201608092280.2%
 
201506092230.2%
 
201612142210.2%
 
201508122210.2%
 
201511092200.2%
 
201610122160.2%
 
201508102120.2%
 
201611092120.2%
 
201405132070.2%
 
Other values (2595)7733858.1%
 
(Missing)4392733.0%
 
ValueCountFrequency (%) 
197201131< 0.1%
 
197205251< 0.1%
 
1977011460604.6%
 
197701153< 0.1%
 
1977012210< 0.1%
 
197701293< 0.1%
 
1977021320< 0.1%
 
1977021926< 0.1%
 
1977022225< 0.1%
 
1977030523< 0.1%
 
ValueCountFrequency (%) 
201612291170.1%
 
201612281230.1%
 
20161227850.1%
 
2016122619< 0.1%
 
201612257< 0.1%
 
2016122412< 0.1%
 
20161223720.1%
 
201612221530.1%
 
201612211920.1%
 
20161220670.1%
 

Cert Issue Date
Real number (ℝ≥0)

MISSING

Distinct11502
Distinct (%)14.1%
Missing51433
Missing (%)38.6%
Infinite0
Infinite (%)0.0%
Mean20058882.07
Minimum19460529
Maximum20161229
Zeros0
Zeros (%)0.0%
Memory size1.0 MiB
2022-01-09T01:20:15.754721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19460529
5-th percentile19790417.2
Q120010828
median20110512
Q320150319
95-th percentile20160915
Maximum20161229
Range700700
Interquartile range (IQR)139491

Descriptive statistics

Standard deviation118823.3645
Coefficient of variation (CV)0.005923728155
Kurtosis1.630621884
Mean20058882.07
Median Absolute Deviation (MAD)40692
Skewness-1.497833427
Sum1.639873786e+12
Variance1.411899195e+10
MonotocityNot monotonic
2022-01-09T01:20:15.848454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20161212990.1%
 
20151205980.1%
 
20151212940.1%
 
20161031910.1%
 
20151007910.1%
 
20151219880.1%
 
20150630840.1%
 
20161221830.1%
 
20160920810.1%
 
20161020810.1%
 
20161019800.1%
 
20151005800.1%
 
20150817800.1%
 
20161101770.1%
 
20150720770.1%
 
20151130760.1%
 
20151230760.1%
 
20161018760.1%
 
20151203740.1%
 
20150727740.1%
 
20130401740.1%
 
20161214740.1%
 
20160107730.1%
 
20151210730.1%
 
20161213730.1%
 
Other values (11477)7972659.9%
 
(Missing)5143338.6%
 
ValueCountFrequency (%) 
194605291< 0.1%
 
194607261< 0.1%
 
194609051< 0.1%
 
194705211< 0.1%
 
194709241< 0.1%
 
194710031< 0.1%
 
194712111< 0.1%
 
194712301< 0.1%
 
194803191< 0.1%
 
194904251< 0.1%
 
ValueCountFrequency (%) 
2016122938< 0.1%
 
2016122865< 0.1%
 
2016122740< 0.1%
 
2016122332< 0.1%
 
20161222710.1%
 
20161221830.1%
 
2016122018< 0.1%
 
2016121957< 0.1%
 
2016121659< 0.1%
 
2016121538< 0.1%
 

Certification Requested
Categorical

HIGH CARDINALITY
MISSING

Distinct181
Distinct (%)0.2%
Missing55743
Missing (%)41.9%
Memory size1.0 MiB
1N
22936 
1
18090 
1NU
8305 
42
8245 
1U
4612 
Other values (176)
15255 
ValueCountFrequency (%) 
1N 2293617.2%
 
11809013.6%
 
1NU 83056.2%
 
4282456.2%
 
1U 46123.5%
 
1T 41943.1%
 
3115591.2%
 
48A 13711.0%
 
1B 13481.0%
 
4310460.8%
 
1NA 7400.6%
 
9A 5850.4%
 
1G 4810.4%
 
61313860.3%
 
1A 3430.3%
 
1C 3430.3%
 
412380.2%
 
3142230.2%
 
401740.1%
 
4431720.1%
 
301660.1%
 
4341380.1%
 
61301350.1%
 
48B 1290.1%
 
21070.1%
 
Other values (156)13771.0%
 
(Missing)5574341.9%
 
2022-01-09T01:20:16.146966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique70 ?
Unique (%)0.1%
2022-01-09T01:20:16.233731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length3
Mean length5.049419609
Min length1

Overview of Unicode Properties

Unique unicode characters23
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
35510852.8%
 
n11148616.6%
 
1649919.7%
 
a557438.3%
 
N320504.8%
 
U130131.9%
 
4127871.9%
 
285681.3%
 
346630.7%
 
T41960.6%
 
A31430.5%
 
816950.3%
 
B14940.2%
 
69300.1%
 
97460.1%
 
06600.1%
 
G5310.1%
 
C4630.1%
 
5122< 0.1%
 
W55< 0.1%
 
725< 0.1%
 
O23< 0.1%
 
P20< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Space Separator35510852.8%
 
Lowercase Letter16722924.9%
 
Decimal Number9518714.2%
 
Uppercase Letter549888.2%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
16499168.3%
 
41278713.4%
 
285689.0%
 
346634.9%
 
816951.8%
 
69301.0%
 
97460.8%
 
06600.7%
 
51220.1%
 
725< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N3205058.3%
 
U1301323.7%
 
T41967.6%
 
A31435.7%
 
B14942.7%
 
G5311.0%
 
C4630.8%
 
W550.1%
 
O23< 0.1%
 
P20< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
355108100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n11148666.7%
 
a5574333.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common45029567.0%
 
Latin22221733.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
35510878.9%
 
16499114.4%
 
4127872.8%
 
285681.9%
 
346631.0%
 
816950.4%
 
69300.2%
 
97460.2%
 
06600.1%
 
5122< 0.1%
 
725< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n11148650.2%
 
a5574325.1%
 
N3205014.4%
 
U130135.9%
 
T41961.9%
 
A31431.4%
 
B14940.7%
 
G5310.2%
 
C4630.2%
 
W55< 0.1%
 
O23< 0.1%
 
P20< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII672512100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
35510852.8%
 
n11148616.6%
 
1649919.7%
 
a557438.3%
 
N320504.8%
 
U130131.9%
 
4127871.9%
 
285681.3%
 
346630.7%
 
T41960.6%
 
A31430.5%
 
816950.3%
 
B14940.2%
 
69300.1%
 
97460.1%
 
06600.1%
 
G5310.1%
 
C4630.1%
 
5122< 0.1%
 
W55< 0.1%
 
725< 0.1%
 
O23< 0.1%
 
P20< 0.1%
 

Type Aircraft
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)< 0.1%
Missing43927
Missing (%)33.0%
Infinite0
Infinite (%)0.0%
Mean4.23736542
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size1.0 MiB
2022-01-09T01:20:16.299555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median4
Q34
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9019209575
Coefficient of variation (CV)0.2128494638
Kurtosis5.502488415
Mean4.23736542
Median Absolute Deviation (MAD)0
Skewness0.3155437799
Sum378223
Variance0.8134614136
MonotocityNot monotonic
2022-01-09T01:20:16.367373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
46648349.9%
 
5111668.4%
 
671285.4%
 
216991.3%
 
116671.3%
 
86520.5%
 
73520.3%
 
91020.1%
 
310< 0.1%
 
(Missing)4392733.0%
 
ValueCountFrequency (%) 
116671.3%
 
216991.3%
 
310< 0.1%
 
46648349.9%
 
5111668.4%
 
671285.4%
 
73520.3%
 
86520.5%
 
91020.1%
 
ValueCountFrequency (%) 
91020.1%
 
86520.5%
 
73520.3%
 
671285.4%
 
5111668.4%
 
46648349.9%
 
310< 0.1%
 
216991.3%
 
116671.3%
 

Type Engine
Real number (ℝ≥0)

MISSING
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing43927
Missing (%)33.0%
Infinite0
Infinite (%)0.0%
Mean2.126452235
Minimum0
Maximum11
Zeros3100
Zeros (%)2.3%
Memory size1.0 MiB
2022-01-09T01:20:16.433970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile8
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.516458035
Coefficient of variation (CV)1.183406801
Kurtosis2.73325513
Mean2.126452235
Median Absolute Deviation (MAD)0
Skewness2.016392545
Sum189805
Variance6.332561044
MonotocityNot monotonic
2022-01-09T01:20:16.508738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
16597849.5%
 
849073.7%
 
548323.6%
 
1034042.6%
 
031002.3%
 
226432.0%
 
319501.5%
 
717851.3%
 
46440.5%
 
1113< 0.1%
 
62< 0.1%
 
91< 0.1%
 
(Missing)4392733.0%
 
ValueCountFrequency (%) 
031002.3%
 
16597849.5%
 
226432.0%
 
319501.5%
 
46440.5%
 
548323.6%
 
62< 0.1%
 
717851.3%
 
849073.7%
 
91< 0.1%
 
ValueCountFrequency (%) 
1113< 0.1%
 
1034042.6%
 
91< 0.1%
 
849073.7%
 
717851.3%
 
62< 0.1%
 
548323.6%
 
46440.5%
 
319501.5%
 
226432.0%
 

Status Code
Categorical

Distinct26
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size1.0 MiB
V
72794 
5
43869 
9
 
5242
28
 
3138
25
 
2964
Other values (21)
 
5178
ValueCountFrequency (%) 
V 7279454.7%
 
54386932.9%
 
952423.9%
 
2831382.4%
 
2529642.2%
 
2113991.1%
 
248500.6%
 
78350.6%
 
R 7680.6%
 
264740.4%
 
M 3740.3%
 
271260.1%
 
21050.1%
 
Z 57< 0.1%
 
1750< 0.1%
 
1045< 0.1%
 
331< 0.1%
 
419< 0.1%
 
1913< 0.1%
 
1112< 0.1%
 
236< 0.1%
 
126< 0.1%
 
N 5< 0.1%
 
D 1< 0.1%
 
W 1< 0.1%
 
2022-01-09T01:20:16.598160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2022-01-09T01:20:16.689696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length1.623834337
Min length1

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
7400034.2%
 
V7279433.7%
 
54683321.7%
 
290704.2%
 
952552.4%
 
831381.5%
 
115370.7%
 
710110.5%
 
48690.4%
 
R7680.4%
 
64740.2%
 
M3740.2%
 
Z57< 0.1%
 
045< 0.1%
 
337< 0.1%
 
N5< 0.1%
 
n2< 0.1%
 
D1< 0.1%
 
W1< 0.1%
 
a1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter7400034.2%
 
Space Separator7400034.2%
 
Decimal Number6826931.6%
 
Lowercase Letter3< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
V7279498.4%
 
R7681.0%
 
M3740.5%
 
Z570.1%
 
N5< 0.1%
 
D1< 0.1%
 
W1< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
74000100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
54683368.6%
 
2907013.3%
 
952557.7%
 
831384.6%
 
115372.3%
 
710111.5%
 
48691.3%
 
64740.7%
 
0450.1%
 
3370.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n266.7%
 
a133.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common14226965.8%
 
Latin7400334.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
V7279498.4%
 
R7681.0%
 
M3740.5%
 
Z570.1%
 
N5< 0.1%
 
n2< 0.1%
 
D1< 0.1%
 
W1< 0.1%
 
a1< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
7400052.0%
 
54683332.9%
 
290706.4%
 
952553.7%
 
831382.2%
 
115371.1%
 
710110.7%
 
48690.6%
 
64740.3%
 
045< 0.1%
 
337< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII216272100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
7400034.2%
 
V7279433.7%
 
54683321.7%
 
290704.2%
 
952552.4%
 
831381.5%
 
115370.7%
 
710110.5%
 
48690.4%
 
R7680.4%
 
64740.2%
 
M3740.2%
 
Z57< 0.1%
 
045< 0.1%
 
337< 0.1%
 
N5< 0.1%
 
n2< 0.1%
 
D1< 0.1%
 
W1< 0.1%
 
a1< 0.1%
 

Mode S Code
Real number (ℝ≥0)

Distinct133185
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean51554648.14
Minimum50000010
Maximum53373705
Zeros0
Zeros (%)0.0%
Memory size1.0 MiB
2022-01-09T01:20:16.803023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum50000010
5-th percentile50105037
Q150606413
median51477722
Q352415661
95-th percentile53215372.4
Maximum53373705
Range3373695
Interquartile range (IQR)1809248

Descriptive statistics

Standard deviation1010772.145
Coefficient of variation (CV)0.01960583927
Kurtosis-1.228389791
Mean51554648.14
Median Absolute Deviation (MAD)906307
Skewness0.1293381853
Sum6.866305813e+12
Variance1.021660329e+12
MonotocityNot monotonic
2022-01-09T01:20:16.916529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
500000131< 0.1%
 
525261741< 0.1%
 
517207421< 0.1%
 
531633201< 0.1%
 
531627321< 0.1%
 
531634611< 0.1%
 
531637071< 0.1%
 
531630121< 0.1%
 
505575401< 0.1%
 
505541601< 0.1%
 
505557411< 0.1%
 
505540701< 0.1%
 
505343561< 0.1%
 
502156521< 0.1%
 
516360461< 0.1%
 
526377201< 0.1%
 
531037531< 0.1%
 
501544301< 0.1%
 
525333701< 0.1%
 
511021011< 0.1%
 
520174271< 0.1%
 
505560201< 0.1%
 
514562661< 0.1%
 
507127201< 0.1%
 
526376641< 0.1%
 
Other values (133160)133160> 99.9%
 
ValueCountFrequency (%) 
500000101< 0.1%
 
500000121< 0.1%
 
500000131< 0.1%
 
500000211< 0.1%
 
500000331< 0.1%
 
500000351< 0.1%
 
500000361< 0.1%
 
500000411< 0.1%
 
500000431< 0.1%
 
500000441< 0.1%
 
ValueCountFrequency (%) 
533737051< 0.1%
 
533736701< 0.1%
 
533736631< 0.1%
 
533736601< 0.1%
 
533736521< 0.1%
 
533736511< 0.1%
 
533736501< 0.1%
 
533736471< 0.1%
 
533736441< 0.1%
 
533736431< 0.1%
 

Fractional Ownership
Categorical

MISSING

Distinct1
Distinct (%)4.8%
Missing133165
Missing (%)> 99.9%
Memory size1.0 MiB
Y
21 
ValueCountFrequency (%) 
Y21< 0.1%
 
(Missing)133165> 99.9%
 
2022-01-09T01:20:17.002736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-01-09T01:20:17.049627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:17.103411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.999684652
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n26633066.7%
 
a13316533.3%
 
Y21< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter399495> 99.9%
 
Uppercase Letter21< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n26633066.7%
 
a13316533.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y21100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin399516100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n26633066.7%
 
a13316533.3%
 
Y21< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII399516100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n26633066.7%
 
a13316533.3%
 
Y21< 0.1%
 

Airworthiness Date
Real number (ℝ≥0)

MISSING

Distinct17390
Distinct (%)24.7%
Missing62700
Missing (%)47.1%
Infinite0
Infinite (%)0.0%
Mean19822614.69
Minimum19000217
Maximum20161203
Zeros0
Zeros (%)0.0%
Memory size1.0 MiB
2022-01-09T01:20:17.185754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19000217
5-th percentile19560308
Q119660422
median19781003
Q320010924
95-th percentile20150528
Maximum20161203
Range1160986
Interquartile range (IQR)350502

Descriptive statistics

Standard deviation195610.504
Coefficient of variation (CV)0.009868047534
Kurtosis-1.233144776
Mean19822614.69
Median Absolute Deviation (MAD)169307
Skewness0.256651657
Sum1.397216819e+12
Variance3.826346926e+10
MonotocityNot monotonic
2022-01-09T01:20:17.305194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
195607161400.1%
 
19560714890.1%
 
1961102450< 0.1%
 
1956071550< 0.1%
 
1979081549< 0.1%
 
1959102748< 0.1%
 
1960103148< 0.1%
 
1956050546< 0.1%
 
1956071246< 0.1%
 
1956062945< 0.1%
 
1956071144< 0.1%
 
1956071043< 0.1%
 
1956063043< 0.1%
 
1979111240< 0.1%
 
1956052640< 0.1%
 
1956071340< 0.1%
 
1956061640< 0.1%
 
1956051238< 0.1%
 
1956070738< 0.1%
 
1956052538< 0.1%
 
1956032337< 0.1%
 
1956051936< 0.1%
 
1956070636< 0.1%
 
1956062835< 0.1%
 
1956040635< 0.1%
 
Other values (17365)6929252.0%
 
(Missing)6270047.1%
 
ValueCountFrequency (%) 
190002171< 0.1%
 
191611041< 0.1%
 
192207071< 0.1%
 
192705251< 0.1%
 
192908121< 0.1%
 
193106291< 0.1%
 
193203141< 0.1%
 
193511121< 0.1%
 
193601051< 0.1%
 
193604211< 0.1%
 
ValueCountFrequency (%) 
201612031< 0.1%
 
201612013< 0.1%
 
201611303< 0.1%
 
201611291< 0.1%
 
201611282< 0.1%
 
201611261< 0.1%
 
201611233< 0.1%
 
201611221< 0.1%
 
201611213< 0.1%
 
201611202< 0.1%
 

Other Name 1
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing133186
Missing (%)100.0%
Memory size1.0 MiB

Other Name 2
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing133186
Missing (%)100.0%
Memory size1.0 MiB

Other Name 3
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing133186
Missing (%)100.0%
Memory size1.0 MiB

Other Name 4
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing133186
Missing (%)100.0%
Memory size1.0 MiB

Other Name 5
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing133186
Missing (%)100.0%
Memory size1.0 MiB

Expiration Date
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct549
Distinct (%)0.6%
Missing36507
Missing (%)27.4%
Infinite0
Infinite (%)0.0%
Mean22309406
Minimum19790911
Maximum99999999
Zeros0
Zeros (%)0.0%
Memory size1.0 MiB
2022-01-09T01:20:17.398922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19790911
5-th percentile20160916.9
Q120170831
median20180731
Q320190531
95-th percentile20200331
Maximum99999999
Range80209088
Interquartile range (IQR)19700

Descriptive statistics

Standard deviation12864361.91
Coefficient of variation (CV)0.576633995
Kurtosis32.50157035
Mean22309406
Median Absolute Deviation (MAD)9822
Skewness5.873743209
Sum2.156851063e+12
Variance1.654918073e+14
MonotocityNot monotonic
2022-01-09T01:20:17.508271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2018043032812.5%
 
2018103132522.4%
 
2018073131392.4%
 
2018013130612.3%
 
2019013130562.3%
 
2019103126522.0%
 
9999999925801.9%
 
2019073125451.9%
 
2018083124901.9%
 
2019043024201.8%
 
2017103124001.8%
 
2017073123821.8%
 
2018123121991.7%
 
2019113021901.6%
 
2020013121141.6%
 
2019123121021.6%
 
2018063020101.5%
 
2018113019981.5%
 
2018022819711.5%
 
2018053119541.5%
 
2018093019491.5%
 
2018033119401.5%
 
2019093019141.4%
 
2019083118391.4%
 
2017083117421.3%
 
Other values (524)3749928.2%
 
(Missing)3650727.4%
 
ValueCountFrequency (%) 
197909111< 0.1%
 
197912281< 0.1%
 
198703011< 0.1%
 
198706081< 0.1%
 
198802022< 0.1%
 
198911191< 0.1%
 
199108051< 0.1%
 
199401252< 0.1%
 
199802261< 0.1%
 
2011033146< 0.1%
 
ValueCountFrequency (%) 
9999999925801.9%
 
202005315490.4%
 
2020043016031.2%
 
202003319200.7%
 
202002298000.6%
 
2020013121141.6%
 
2019123121021.6%
 
2019113021901.6%
 
2019103126522.0%
 
2019093019141.4%
 

Unique ID
Real number (ℝ≥0)

MISSING

Distinct89259
Distinct (%)100.0%
Missing43927
Missing (%)33.0%
Infinite0
Infinite (%)0.0%
Mean540455.812
Minimum2
Maximum1239445
Zeros0
Zeros (%)0.0%
Memory size1.0 MiB
2022-01-09T01:20:17.633010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile44017
Q1230553
median469659
Q3841356.5
95-th percentile1190761.2
Maximum1239445
Range1239443
Interquartile range (IQR)610803.5

Descriptive statistics

Standard deviation365580.0503
Coefficient of variation (CV)0.6764291218
Kurtosis-1.071898284
Mean540455.812
Median Absolute Deviation (MAD)290608
Skewness0.3660445438
Sum4.824054533e+10
Variance1.336487732e+11
MonotocityNot monotonic
2022-01-09T01:20:17.747036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
900001< 0.1%
 
303011< 0.1%
 
8603011< 0.1%
 
7727111< 0.1%
 
3140801< 0.1%
 
1045591< 0.1%
 
2900551< 0.1%
 
3003011< 0.1%
 
2434391< 0.1%
 
1357721< 0.1%
 
403011< 0.1%
 
3847781< 0.1%
 
3601081< 0.1%
 
3600981< 0.1%
 
3600681< 0.1%
 
3403411< 0.1%
 
1418151< 0.1%
 
3638161< 0.1%
 
3600381< 0.1%
 
3600081< 0.1%
 
8502811< 0.1%
 
602911< 0.1%
 
8903311< 0.1%
 
12203881< 0.1%
 
602811< 0.1%
 
Other values (89234)8923467.0%
 
(Missing)4392733.0%
 
ValueCountFrequency (%) 
21< 0.1%
 
41< 0.1%
 
161< 0.1%
 
201< 0.1%
 
231< 0.1%
 
241< 0.1%
 
311< 0.1%
 
421< 0.1%
 
441< 0.1%
 
461< 0.1%
 
ValueCountFrequency (%) 
12394451< 0.1%
 
12394441< 0.1%
 
12394041< 0.1%
 
12394001< 0.1%
 
12393841< 0.1%
 
12392711< 0.1%
 
12392671< 0.1%
 
12392321< 0.1%
 
12392081< 0.1%
 
12391391< 0.1%
 

Kit MFR Code
Real number (ℝ≥0)

MISSING

Distinct580
Distinct (%)17.9%
Missing129937
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean452.511542
Minimum4
Maximum1268
Zeros0
Zeros (%)0.0%
Memory size1.0 MiB
2022-01-09T01:20:17.832562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile113
Q1157
median368
Q3660
95-th percentile1121
Maximum1268
Range1264
Interquartile range (IQR)503

Descriptive statistics

Standard deviation332.9188498
Coefficient of variation (CV)0.7357134987
Kurtosis-0.4387089769
Mean452.511542
Median Absolute Deviation (MAD)211
Skewness0.8482174665
Sum1470210
Variance110834.9605
MonotocityNot monotonic
2022-01-09T01:20:17.941912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1572280.2%
 
4621580.1%
 
3441410.1%
 
1531370.1%
 
4031340.1%
 
1551320.1%
 
5401140.1%
 
179870.1%
 
152850.1%
 
158710.1%
 
11350< 0.1%
 
20244< 0.1%
 
17842< 0.1%
 
76042< 0.1%
 
84741< 0.1%
 
55937< 0.1%
 
18232< 0.1%
 
22731< 0.1%
 
118429< 0.1%
 
67226< 0.1%
 
45522< 0.1%
 
90417< 0.1%
 
108316< 0.1%
 
36916< 0.1%
 
97616< 0.1%
 
Other values (555)15011.1%
 
(Missing)12993797.6%
 
ValueCountFrequency (%) 
41< 0.1%
 
63< 0.1%
 
94< 0.1%
 
164< 0.1%
 
191< 0.1%
 
202< 0.1%
 
213< 0.1%
 
272< 0.1%
 
292< 0.1%
 
304< 0.1%
 
ValueCountFrequency (%) 
12681< 0.1%
 
12671< 0.1%
 
12662< 0.1%
 
12651< 0.1%
 
12641< 0.1%
 
12634< 0.1%
 
12612< 0.1%
 
12602< 0.1%
 
12562< 0.1%
 
12551< 0.1%
 

Kit Model
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing133186
Missing (%)100.0%
Memory size1.0 MiB

Mose S Code Hex
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing133186
Missing (%)100.0%
Memory size1.0 MiB

Interactions

2022-01-09T01:19:56.393960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:56.484746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:56.570515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:56.654854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:56.802489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:56.887262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:56.970042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:57.047001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:57.130780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:57.212068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:57.288370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:57.368156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:57.446454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:57.530734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:57.619497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:57.710254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:57.798020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:57.890771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:57.981528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:58.066301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:58.156061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:58.245821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:58.331619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:58.417866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:58.502639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:58.585949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:58.677438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:58.770214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:58.856984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:58.949780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:59.112604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:59.194772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:59.282511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:59.373572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:59.459258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:59.548030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:59.633800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:59.713587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:59.797363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:59.880141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:19:59.957933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:00.042706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:00.124487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:00.199808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:00.284582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:00.368359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:00.445996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:00.525689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:00.600758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:00.691707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:00.784699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:00.881631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:00.970394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:01.065143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:01.157471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:01.242819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:01.335905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:01.429689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:01.519486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:01.605723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:01.695080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:01.778304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:01.956125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:02.045757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:02.133061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:02.225844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:02.314174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:02.396330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:02.486121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:02.575446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:02.660738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:02.750499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:02.838828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:02.914527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:02.991770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:03.071056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:03.141077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:03.223856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:03.302613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:03.375418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:03.455205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:03.532997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:03.607796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:03.678541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:03.755939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:03.841414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:03.931165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:04.018929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:04.104605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:04.193862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:04.282825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:04.363662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:04.453103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:04.542338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:04.626628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:04.710630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:04.795753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:04.878894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:04.965636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:05.050644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:05.133328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:05.328323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:05.422518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:05.501038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:05.585811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:05.673116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:05.755727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:05.840966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:05.930816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:06.008104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:06.085969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:06.169720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:06.247506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:06.330713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:06.409381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:06.480852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:06.565873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:06.655074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:06.732896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:06.807230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:06.891437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:06.974811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:07.061295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:07.149822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:07.227479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:07.317118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:07.400404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:07.477563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:07.565617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:07.654411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:07.737746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:07.820774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:07.900834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:07.981743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:08.071470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:08.154248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:08.233038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:08.317811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:08.403581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:08.480376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:08.565149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:08.649922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:08.730706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:08.813484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-01-09T01:20:18.035639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-09T01:20:18.219554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-09T01:20:18.418390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-09T01:20:18.584033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-01-09T01:20:18.709001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-01-09T01:20:09.388974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:10.248693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:11.430377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T01:20:11.935035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Sample

First rows

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Last rows

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